import types
import pandas as pd
from botocore.client import Config # For Importing Data from IBM Storage
import ibm_boto3
import requests #To handle HTTPS requests
from bs4 import BeautifulSoup #To extract data out of HTML/XML
from pandas.io.json import json_normalize#Transform json file to pandas Data Frame
from sklearn.cluster import KMeans #import k-means from clustering
import matplotlib.cm as cm#Matplotlib and its plotting modules
import matplotlib.colors as colors
import json # To read json files
from math import cos, asin, sqrt, radians, sin # To calculate the Haversine Formula
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans # Kmeans Cluster Algorihm
from sklearn.metrics import silhouette_score # To determine best K for the KMeans
import numpy as np

#!conda install -c conda-forge geopy --yes
from geopy.geocoders import Nominatim # Convert an address to Latitude an Longitude

#!conda install -c conda-forge folium
import folium #Map Rendering Library
from folium import plugins

def __iter__(self): return 0

Obtaining New York crime data from NYPD Open Data

# @hidden_cell
# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.
# You might want to remove those credentials before you share the notebook.
client_68e7d9056f2c4416a8662970f50e8c18 = ibm_boto3.client(service_name='s3',
    ibm_api_key_id='_anf1yfVoU1u4TWBMNgzdOSYc_4LpX9oezr0-E8LbhmH',
    ibm_auth_endpoint="https://iam.cloud.ibm.com/oidc/token",
    config=Config(signature_version='oauth'),
    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')
body = client_68e7d9056f2c4416a8662970f50e8c18.get_object(Bucket='applieddatasciencecapstoneproject-donotdelete-pr-zbsuvzjjiph9pp',Key='NYPD_Complaint_Data_Historic (1).csv')['Body']
# add missing __iter__ method, so pandas accepts body as file-like object
if not hasattr(body, "__iter__"): body.__iter__ = types.MethodType( __iter__, body )

#NEW YORK CRIME DATA
nycrime_data= pd.read_csv(body)

/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3166: DtypeWarning: Columns (20) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

#Checking columns and Data Pre Processing
nycrime_data.columns

Index(['CMPLNT_NUM', 'CMPLNT_FR_DT', 'CMPLNT_FR_TM', 'CMPLNT_TO_DT',
       'CMPLNT_TO_TM', 'ADDR_PCT_CD', 'RPT_DT', 'KY_CD', 'OFNS_DESC', 'PD_CD',
       'PD_DESC', 'CRM_ATPT_CPTD_CD', 'LAW_CAT_CD', 'BORO_NM',
       'LOC_OF_OCCUR_DESC', 'PREM_TYP_DESC', 'JURIS_DESC', 'JURISDICTION_CODE',
       'PARKS_NM', 'HADEVELOPT', 'HOUSING_PSA', 'X_COORD_CD', 'Y_COORD_CD',
       'SUSP_AGE_GROUP', 'SUSP_RACE', 'SUSP_SEX', 'TRANSIT_DISTRICT',
       'Latitude', 'Longitude', 'Lat_Lon', 'PATROL_BORO', 'STATION_NAME',
       'VIC_AGE_GROUP', 'VIC_RACE', 'VIC_SEX'],
      dtype='object')

Data PreProcessing-Removing unnecessary columns

nycrime_data=nycrime_data.drop(columns=['CMPLNT_NUM','CMPLNT_FR_TM', 'CMPLNT_TO_DT',
       'CMPLNT_TO_TM', 'ADDR_PCT_CD', 'RPT_DT', 'KY_CD','PD_CD',
       'PD_DESC', 'CRM_ATPT_CPTD_CD', 'LAW_CAT_CD','LOC_OF_OCCUR_DESC', 'PREM_TYP_DESC', 'JURIS_DESC', 'JURISDICTION_CODE',
       'PARKS_NM', 'HADEVELOPT', 'HOUSING_PSA', 'X_COORD_CD', 'Y_COORD_CD',
       'SUSP_AGE_GROUP', 'SUSP_RACE', 'SUSP_SEX', 'TRANSIT_DISTRICT', 'Lat_Lon', 'PATROL_BORO', 'STATION_NAME','VIC_AGE_GROUP', 'VIC_RACE', 'VIC_SEX'])

#Selecting Data for just last 1 year
nycrime_data.head()
nycrime_data.rename(columns={"CMPLNT_FR_DT":"DATE","OFNS_DESC":"OFFENSE","BORO_NM":"BOROUGH"},inplace=True)
nycrime_data['DATE']=pd.to_datetime(nycrime_data['DATE'])
start_date='2020-01-01'
end_date='2021-01-01'
mask=(nycrime_data['DATE']>start_date)&(nycrime_data['DATE']<=end_date)
nycrime_data=nycrime_data.loc[start_date:end_date]
nycrime_data.shape

(1000, 5)

#Drop any NaN values
nycrime_data.dropna(inplace=True)
nycrime_data.shape
nycrime_data.head()

	DATE	OFFENSE	BOROUGH	Latitude	Longitude
2020000	2019-04-12	CRIMINAL MISCHIEF & RELATED OF	MANHATTAN	40.764007	-73.996005
2020001	2019-04-12	DANGEROUS DRUGS	QUEENS	40.602195	-73.749104
2020002	2019-04-12	HARRASSMENT 2	QUEENS	40.751482	-73.822033
2020003	2019-04-12	PETIT LARCENY	MANHATTAN	40.718027	-73.999958
2020004	2019-04-12	PETIT LARCENY	BROOKLYN	40.630754	-73.977167

Mapping All the Offenses in New York

NY_map=folium.Map(location=[40.71, -74.0],zoom_start=10)
incidents=folium.plugins.MarkerCluster().add_to(NY_map)
for lat,lng,label in zip(nycrime_data.Latitude,nycrime_data.Longitude,nycrime_data.OFFENSE):
    folium.features.CircleMarker(
    [lat,lng],radius=5,color='red',fill=True,fill_color='blue',fill_opacity=0.6,popup=label).add_to(incidents)
NY_map

Make this Notebook Trusted to load map: File -> Trust Notebook

#Finding Borough with Largest Number of Crimes Reported
borough_data=nycrime_data.groupby('BOROUGH')['OFFENSE'].count()
borough_data=borough_data.to_frame()

Brooklyn Has the Highest Number of Crimes Commited and Staten Island has the least number of crimes commited

borough_data.plot(kind='bar',figsize=(10,10))
plt.xlabel('Borough')
plt.ylabel('Number of Crimes')
plt.title('Crime Rates in Boroughs')
plt.show()

Finding the Neighbourhood with the largest number of crimes

#Checking in which neighborhood the crimes where commited based on the latitude and longitude
#Getting Neighbourhood Latitude and Longitude
!wget -q -O 'newyork_data.json' https://cocl.us/new_york_dataset
print('Data downloaded!')

Data downloaded!

with open ('newyork_data.json') as json_data:
    newyork_data =json.load(json_data)
newyork_data

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    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.84720052054902,
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    'annoline3': None,
    'annoangle': 0.0,
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    'annoangle': 0.0,
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    'annoangle': 0.0,
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    'bbox': [-73.85931863221647,
     40.86578787802982,
     -73.85931863221647,
     40.86578787802982]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.300',
   'geometry': {'type': 'Point',
    'coordinates': [-73.90152264513144, 40.8703923914147]},
   'geometry_name': 'geom',
   'properties': {'name': 'Kingsbridge Heights',
    'stacked': 2,
    'annoline1': 'Kingsbridge',
    'annoline2': 'Heights',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.90152264513144,
     40.8703923914147,
     -73.90152264513144,
     40.8703923914147]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.301',
   'geometry': {'type': 'Point',
    'coordinates': [-73.94817709920184, 40.64692606658579]},
   'geometry_name': 'geom',
   'properties': {'name': 'Erasmus',
    'stacked': 1,
    'annoline1': 'Erasmus',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Brooklyn',
    'bbox': [-73.94817709920184,
     40.64692606658579,
     -73.94817709920184,
     40.64692606658579]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.302',
   'geometry': {'type': 'Point',
    'coordinates': [-74.00011136202637, 40.75665808227519]},
   'geometry_name': 'geom',
   'properties': {'name': 'Hudson Yards',
    'stacked': 2,
    'annoline1': 'Hudson',
    'annoline2': 'Yards',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Manhattan',
    'bbox': [-74.00011136202637,
     40.75665808227519,
     -74.00011136202637,
     40.75665808227519]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.303',
   'geometry': {'type': 'Point',
    'coordinates': [-73.80553002968718, 40.58733774018741]},
   'geometry_name': 'geom',
   'properties': {'name': 'Hammels',
    'stacked': 1,
    'annoline1': 'Hammels',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Queens',
    'bbox': [-73.80553002968718,
     40.58733774018741,
     -73.80553002968718,
     40.58733774018741]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.304',
   'geometry': {'type': 'Point',
    'coordinates': [-73.76596781445627, 40.611321691283834]},
   'geometry_name': 'geom',
   'properties': {'name': 'Bayswater',
    'stacked': 1,
    'annoline1': 'Bayswater',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Queens',
    'bbox': [-73.76596781445627,
     40.611321691283834,
     -73.76596781445627,
     40.611321691283834]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.305',
   'geometry': {'type': 'Point',
    'coordinates': [-73.94563070334091, 40.756091297094706]},
   'geometry_name': 'geom',
   'properties': {'name': 'Queensbridge',
    'stacked': 1,
    'annoline1': 'Queensbridge',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Queens',
    'bbox': [-73.94563070334091,
     40.756091297094706,
     -73.94563070334091,
     40.756091297094706]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.306',
   'geometry': {'type': 'Point',
    'coordinates': [-74.08173992211962, 40.61731079252983]},
   'geometry_name': 'geom',
   'properties': {'name': 'Fox Hills',
    'stacked': 2,
    'annoline1': 'Fox',
    'annoline2': 'Hills',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Staten Island',
    'bbox': [-74.08173992211962,
     40.61731079252983,
     -74.08173992211962,
     40.61731079252983]}}],
 'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:EPSG::4326'}},
 'bbox': [-74.2492599487305,
  40.5033187866211,
  -73.7061614990234,
  40.9105606079102]}

#As the information we desire is under the features tab, getting just the features from the json file
nyneigh_data=newyork_data['features']
nyneigh_data[0]

{'type': 'Feature',
 'id': 'nyu_2451_34572.1',
 'geometry': {'type': 'Point',
  'coordinates': [-73.84720052054902, 40.89470517661]},
 'geometry_name': 'geom',
 'properties': {'name': 'Wakefield',
  'stacked': 1,
  'annoline1': 'Wakefield',
  'annoline2': None,
  'annoline3': None,
  'annoangle': 0.0,
  'borough': 'Bronx',
  'bbox': [-73.84720052054902,
   40.89470517661,
   -73.84720052054902,
   40.89470517661]}}

#Creating an empty Data Frame
column_name=['Borough','Neighbourhood','Latitude','Longitude']
neigh_data=pd.DataFrame(columns=column_name)
neigh_data

	Borough	Neighbourhood	Latitude	Longitude

#Populating the details from the json file to the DataFrame
for data in nyneigh_data:
    borough=data['properties']['borough']
    neigh=data['properties']['name']
    latlong=data['geometry']['coordinates']
    lat=latlong[1]
    lng=latlong[0]
    neigh_data=neigh_data.append({'Borough':borough,'Neighbourhood':neigh,'Latitude':lat,'Longitude':lng},ignore_index=True)
    

neigh_data.head()

	Borough	Neighbourhood	Latitude	Longitude
0	Bronx	Wakefield	40.894705	-73.847201
1	Bronx	Co-op City	40.874294	-73.829939
2	Bronx	Eastchester	40.887556	-73.827806
3	Bronx	Fieldston	40.895437	-73.905643
4	Bronx	Riverdale	40.890834	-73.912585

Using the Haversine formula to find the distance between two points given the Latitudes and Longitudes

#Function to calculate distance between 2 points
def dist(lat1, long1, lat2, long2):
    """Calculate the great circle distance between two points 
    on the earth"""
    # convert decimal degrees to radians 
    lat1, long1, lat2, long2 = map(radians, [lat1, long1, lat2, long2])
    # haversine formula 
    dlon = long2 - long1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    # Radius of earth in kilometers is 6371
    km = 6371* c
    return km

#Function to find the nearest neighborhood in which the crime occured
def find_neigh(lat, long):
    distances = neigh_data.apply(
        lambda row: dist(lat, long, row['Latitude'], row['Longitude']), 
        axis=1)
    return neigh_data.loc[distances.idxmin(), 'Neighbourhood']

Finding the neighborhood in which the crimes occured

#Creating a new column in the Nycrime Data called the Neighborhood and Finding the nearest neighbourhood in which the crime occured
nycrime_data['Neighbourhood'] = nycrime_data.apply(
    lambda row: find_neigh(row['Latitude'], row['Longitude']), 
    axis=1)
# To check the data frame if it has a new column of hotel name (for each and every member's location in the list)
nycrime_data.head()

	DATE	OFFENSE	BOROUGH	Latitude	Longitude	Neighbourhood
2020000	2019-04-12	CRIMINAL MISCHIEF & RELATED OF	MANHATTAN	40.764007	-73.996005	Clinton
2020001	2019-04-12	DANGEROUS DRUGS	QUEENS	40.602195	-73.749104	Far Rockaway
2020002	2019-04-12	HARRASSMENT 2	QUEENS	40.751482	-73.822033	Queensboro Hill
2020003	2019-04-12	PETIT LARCENY	MANHATTAN	40.718027	-73.999958	Little Italy
2020004	2019-04-12	PETIT LARCENY	BROOKLYN	40.630754	-73.977167	Borough Park

Find the Neighborhood in which there are largest number of crimes

#Finding the neighbrohood with least and largest number of crimes
neighcrime_data=nycrime_data.groupby('Neighbourhood')['OFFENSE'].count()
neighcrime_data=neighcrime_data.to_frame()
print(neighcrime_data.loc[neighcrime_data['OFFENSE'].idxmax()])
print(neighcrime_data.loc[neighcrime_data['OFFENSE'].idxmin()])

OFFENSE    23
Name: East Harlem, dtype: int64
OFFENSE    1
Name: Brooklyn Heights, dtype: int64

neighcrime_data=neighcrime_data.sort_values(by=['OFFENSE'])
neighcrime_data[neighcrime_data['OFFENSE']==1].count()

OFFENSE    38
dtype: int64

East Harlem has the highest number of Crimes reported(23) and 38 other neighbourhoods has the least number of crimes reported(1)

%matplotlib inline
neighcrime_data.plot(kind='bar',stacked=False,figsize=(35,10))
plt.xlabel('Neighborhood')
plt.ylabel('Number of Crimes')
plt.title('Crime Rates in Neighborhood')
plt.show()

neighcrime_data.reset_index(inplace=True)

neighcrime_data.columns

Index(['Neighbourhood', 'OFFENSE'], dtype='object')

#Merging the New York Crime Data with the Neighbourhood Details Table
nycrime_data=pd.merge(nycrime_data,neigh_data[['Neighbourhood','Latitude','Longitude']],on='Neighbourhood',how='left')
nycrime_data=nycrime_data.rename(columns={'Latitude_x':'Crime_Lat','Longitude_x':'Crime_Lon','Latitude_y':'Neigh_Lat','Longitude_y':'Neigh_Lon'})
nycrime_data.head()

	DATE	OFFENSE	BOROUGH	Crime_Lat	Crime_Lon	Neighbourhood	Neigh_Lat	Neigh_Lon
0	2019-04-12	CRIMINAL MISCHIEF & RELATED OF	MANHATTAN	40.764007	-73.996005	Clinton	40.759101	-73.996119
1	2019-04-12	DANGEROUS DRUGS	QUEENS	40.602195	-73.749104	Far Rockaway	40.603134	-73.754980
2	2019-04-12	HARRASSMENT 2	QUEENS	40.751482	-73.822033	Queensboro Hill	40.744572	-73.825809
3	2019-04-12	PETIT LARCENY	MANHATTAN	40.718027	-73.999958	Little Italy	40.719324	-73.997305
4	2019-04-12	PETIT LARCENY	BROOKLYN	40.630754	-73.977167	Borough Park	40.633131	-73.990498

Cholropleth Map of Crime in Neighborhoods

!wget --quiet https://data.beta.nyc/dataset/0ff93d2d-90ba-457c-9f7e-39e47bf2ac5f/resource/35dd04fb-81b3-479b-a074-a27a37888ce7/download/d085e2f8d0b54d4590b1e7d1f35594c1pediacitiesnycneighborhoods.geojson -O neighborhoods.geojson

neigh_geo=r'neighborhoods.geojson'
neigh_geo

'neighborhoods.geojson'

Neigh_map=folium.Map(location=[40.7128, -74.0060],zoom_start=9,width=900,height=700)
Neigh_map.choropleth(
    geo_data=neigh_geo,
    data=neighcrime_data,
    columns=['Neighbourhood','OFFENSE'],
    key_on='feature.properties.neighborhood',
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Crime Rates in Neighborhood',
    nan_fill_color='lightyellow',
    nan_fill_opacity=0.7
)
folium.CircleMarker([40.7957, -73.9389],popup='East Harmel: Highest Crime Neighborhood',radius=2,fill_color='red',fill=True).add_to(Neigh_map)
Neigh_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Finding the desired Neighbourhood having Gym,Grocery Store,Restaurnt,Pharmacy near by. Using Foursquare to get Venues in a Neighbourhood

#Forusquare id and accesstoken
CLIENT_ID = 'BRIW4D0HG0ZSLYSZOGYS554ZR1OO21CFAN2IRWBCLW4SP2E0' # your Foursquare ID
CLIENT_SECRET = 'W3WWKM4XRSZ5ERFA1HMHH3LC53QRKDCUAFTO5CI5VZPR1MNN' # your Foursquare Secret
ACCESS_TOKEN = 'G1FSY0CD3ZWOHAWIGSJONVHQB35N5V1L4SD0PTR030RTICZW' # your FourSquare Access Token
VERSION = '20210101'
LIMIT = 5
print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)

Your credentails:
CLIENT_ID: BRIW4D0HG0ZSLYSZOGYS554ZR1OO21CFAN2IRWBCLW4SP2E0
CLIENT_SECRET:W3WWKM4XRSZ5ERFA1HMHH3LC53QRKDCUAFTO5CI5VZPR1MNN

Defining Function to get near by Venues Using Foursquare API

#Function to get Nearby Venues for each Neighborhoos
def getNearbyVenues(names,latitudes,longitudes,radius=500):
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()['response']['groups'][0]['items']
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

neigh_data.head()

	Borough	Neighbourhood	Latitude	Longitude
0	Bronx	Wakefield	40.894705	-73.847201
1	Bronx	Co-op City	40.874294	-73.829939
2	Bronx	Eastchester	40.887556	-73.827806
3	Bronx	Fieldston	40.895437	-73.905643
4	Bronx	Riverdale	40.890834	-73.912585

Ny_venues = getNearbyVenues(names=neigh_data['Neighbourhood'],
                                   latitudes=neigh_data['Latitude'],
                                   longitudes=neigh_data['Longitude']
                                  )

Wakefield
Co-op City
Eastchester
Fieldston
Riverdale
Kingsbridge
Marble Hill
Woodlawn
Norwood
Williamsbridge
Baychester
Pelham Parkway
City Island
Bedford Park
University Heights
Morris Heights
Fordham
East Tremont
West Farms
High  Bridge
Melrose
Mott Haven
Port Morris
Longwood
Hunts Point
Morrisania
Soundview
Clason Point
Throgs Neck
Country Club
Parkchester
Westchester Square
Van Nest
Morris Park
Belmont
Spuyten Duyvil
North Riverdale
Pelham Bay
Schuylerville
Edgewater Park
Castle Hill
Olinville
Pelham Gardens
Concourse
Unionport
Edenwald
Bay Ridge
Bensonhurst
Sunset Park
Greenpoint
Gravesend
Brighton Beach
Sheepshead Bay
Manhattan Terrace
Flatbush
Crown Heights
East Flatbush
Kensington
Windsor Terrace
Prospect Heights
Brownsville
Williamsburg
Bushwick
Bedford Stuyvesant
Brooklyn Heights
Cobble Hill
Carroll Gardens
Red Hook
Gowanus
Fort Greene
Park Slope
Cypress Hills
East New York
Starrett City
Canarsie
Flatlands
Mill Island
Manhattan Beach
Coney Island
Bath Beach
Borough Park
Dyker Heights
Gerritsen Beach
Marine Park
Clinton Hill
Sea Gate
Downtown
Boerum Hill
Prospect Lefferts Gardens
Ocean Hill
City Line
Bergen Beach
Midwood
Prospect Park South
Georgetown
East Williamsburg
North Side
South Side
Ocean Parkway
Fort Hamilton
Chinatown
Washington Heights
Inwood
Hamilton Heights
Manhattanville
Central Harlem
East Harlem
Upper East Side
Yorkville
Lenox Hill
Roosevelt Island
Upper West Side
Lincoln Square
Clinton
Midtown
Murray Hill
Chelsea
Greenwich Village
East Village
Lower East Side
Tribeca
Little Italy
Soho
West Village
Manhattan Valley
Morningside Heights
Gramercy
Battery Park City
Financial District
Astoria
Woodside
Jackson Heights
Elmhurst
Howard Beach
Corona
Forest Hills
Kew Gardens
Richmond Hill
Flushing
Long Island City
Sunnyside
East Elmhurst
Maspeth
Ridgewood
Glendale
Rego Park
Woodhaven
Ozone Park
South Ozone Park
College Point
Whitestone
Bayside
Auburndale
Little Neck
Douglaston
Glen Oaks
Bellerose
Kew Gardens Hills
Fresh Meadows
Briarwood
Jamaica Center
Oakland Gardens
Queens Village
Hollis
South Jamaica
St. Albans
Rochdale
Springfield Gardens
Cambria Heights
Rosedale
Far Rockaway
Broad Channel
Breezy Point
Steinway
Beechhurst
Bay Terrace
Edgemere
Arverne
Rockaway Beach
Neponsit
Murray Hill
Floral Park
Holliswood
Jamaica Estates
Queensboro Hill
Hillcrest
Ravenswood
Lindenwood
Laurelton
Lefrak City
Belle Harbor
Rockaway Park
Somerville
Brookville
Bellaire
North Corona
Forest Hills Gardens
St. George
New Brighton
Stapleton
Rosebank
West Brighton
Grymes Hill
Todt Hill
South Beach
Port Richmond
Mariner's Harbor
Port Ivory
Castleton Corners
New Springville
Travis
New Dorp
Oakwood
Great Kills
Eltingville
Annadale
Woodrow
Tottenville
Tompkinsville
Silver Lake
Sunnyside
Ditmas Park
Wingate
Rugby
Park Hill
Westerleigh
Graniteville
Arlington
Arrochar
Grasmere
Old Town
Dongan Hills
Midland Beach
Grant City
New Dorp Beach
Bay Terrace
Huguenot
Pleasant Plains
Butler Manor
Charleston
Rossville
Arden Heights
Greenridge
Heartland Village
Chelsea
Bloomfield
Bulls Head
Carnegie Hill
Noho
Civic Center
Midtown South
Richmond Town
Shore Acres
Clifton
Concord
Emerson Hill
Randall Manor
Howland Hook
Elm Park
Remsen Village
New Lots
Paerdegat Basin
Mill Basin
Jamaica Hills
Utopia
Pomonok
Astoria Heights
Claremont Village
Concourse Village
Mount Eden
Mount Hope
Sutton Place
Hunters Point
Turtle Bay
Tudor City
Stuyvesant Town
Flatiron
Sunnyside Gardens
Blissville
Fulton Ferry
Vinegar Hill
Weeksville
Broadway Junction
Dumbo
Manor Heights
Willowbrook
Sandy Ground
Egbertville
Roxbury
Homecrest
Middle Village
Prince's Bay
Lighthouse Hill
Richmond Valley
Malba
Highland Park
Madison
Bronxdale
Allerton
Kingsbridge Heights
Erasmus
Hudson Yards
Hammels
Bayswater
Queensbridge
Fox Hills

Ny_venues['Venue Category'].unique()
Ny_venues.head()

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
0	Wakefield	40.894705	-73.847201	Lollipops Gelato	40.894123	-73.845892	Dessert Shop
1	Wakefield	40.894705	-73.847201	Rite Aid	40.896649	-73.844846	Pharmacy
2	Wakefield	40.894705	-73.847201	Walgreens	40.896528	-73.844700	Pharmacy
3	Wakefield	40.894705	-73.847201	Carvel Ice Cream	40.890487	-73.848568	Ice Cream Shop
4	Wakefield	40.894705	-73.847201	Subway	40.890468	-73.849152	Sandwich Place

Replacing Different type of Restaurants to just Restaurants

Ny_venues=Ny_venues.replace({'Venue Category':r'^(.*?(\bRestaurant\b)[^$]*)$'},{'Venue Category':'Restaurant'},regex=True)
Ny_venues['Venue Category'].unique()

array(['Dessert Shop', 'Pharmacy', 'Ice Cream Shop', 'Sandwich Place',
       'Pizza Place', 'Discount Store', 'Post Office', 'Bagel Shop',
       'Restaurant', 'Diner', 'Donut Shop', 'Plaza', 'Bus Station',
       'Music Venue', 'Park', 'Bank', 'Baseball Field', 'Gourmet Shop',
       'Yoga Studio', 'Coffee Shop', 'Pub', 'Food Truck', 'Soup Place',
       'Nightclub', 'Bar', 'Metro Station', 'Gym / Fitness Center',
       'Frozen Yogurt Shop', 'Grocery Store', 'Wings Joint',
       'Supermarket', 'Juice Bar', 'Gym', 'Café', 'Mobile Phone Shop',
       'Lounge', 'Market', 'Burger Joint', 'Brewery', 'Distillery',
       'Storage Facility', 'BBQ Joint', 'Fish Market', 'Breakfast Spot',
       'Fried Chicken Joint', 'Pool', 'Sports Bar', 'Playground',
       'Athletics & Sports', 'Deli / Bodega', 'Tennis Stadium', 'Bakery',
       'Food', 'Art Museum', 'Spa', 'Cosmetics Shop', 'Laundry Service',
       'Other Great Outdoors', 'Food & Drink Shop', 'Neighborhood',
       'Jazz Club', 'Convenience Store', 'Museum', 'Candy Store',
       'Arts & Crafts Store', 'Antique Shop', 'Bookstore', 'Cocktail Bar',
       'Wine Shop', 'Performing Arts Venue', 'Farmers Market',
       'Taco Place', 'Boutique', 'History Museum', 'Pilates Studio',
       'Butcher', 'Furniture / Home Store', 'Rock Club', 'Pie Shop',
       'Accessories Store', 'Bus Line', 'Paper / Office Supplies Store',
       'Locksmith', 'Beach', 'Bus Stop', 'Skating Rink',
       'Baseball Stadium', 'Monument / Landmark', 'Surf Spot',
       'Hookah Bar', 'Golf Course', 'Dance Studio', 'Department Store',
       'Pet Store', 'Video Store', 'Sports Club', 'Movie Theater',
       'Food Court', 'Kids Store', 'Video Game Store', 'Rental Service',
       'Harbor / Marina', 'Theater', 'Boxing Gym', 'Salad Place',
       'Steakhouse', 'General Entertainment', 'Hotel', 'Wine Bar',
       'Climbing Gym', 'Cycle Studio', 'Beer Bar', 'Library',
       "Doctor's Office", 'Burrito Place', 'Liquor Store', 'Hobby Shop',
       'Outdoors & Recreation', 'Waterfront', 'Opera House',
       'Concert Hall', 'Indie Movie Theater', 'Comedy Club', 'Smoke Shop',
       'Tea Room', 'Cupcake Shop', 'Clothing Store', 'Beer Store',
       'Art Gallery', 'Salon / Barbershop', 'Miscellaneous Shop',
       'Hostel', 'Outdoor Sculpture', 'Cooking School', 'Trail', 'Church',
       'Rental Car Location', 'Hotel Bar', 'Martial Arts School',
       'Nail Salon', 'Bubble Tea Shop', 'Jewelry Store',
       'Sporting Goods Shop', 'Other Nightlife', 'Dive Bar', 'Irish Pub',
       'Farm', 'Scenic Lookout', 'Dosa Place', 'Basketball Court',
       'Intersection', 'Supplement Shop', 'College Basketball Court',
       'College Academic Building', 'Fruit & Vegetable Store',
       'Train Station', 'Sculpture Garden', 'Shopping Mall',
       'Recording Studio', 'Dog Run', 'Pier', 'Tattoo Parlor',
       'Health & Beauty Service', 'Bowling Alley', 'Lawyer',
       'Home Service', 'Nightlife Spot', 'Gastropub', 'Field',
       'Fish & Chips Shop', 'Arcade', 'Construction & Landscaping',
       'Boat or Ferry', 'Optical Shop', 'Business Service',
       'Shipping Store', 'Gas Station', 'Big Box Store', 'Gift Shop',
       'Professional & Other Places', 'Recreation Center', 'Theme Park',
       'Thrift / Vintage Store', 'Historic Site', 'Lake',
       'Child Care Service', 'Adult Boutique', 'Beer Garden',
       'Karaoke Bar', 'Bridge', 'Shoe Store', 'Factory', 'Whisky Bar',
       'Entertainment Service', "Men's Store", 'Campground',
       'Other Repair Shop', 'Rest Area', 'Tennis Court', 'Music School'],
      dtype=object)

#Selecting All the desired Venues from the DF
Ny_venues=Ny_venues.loc[(Ny_venues['Venue Category'].isin(['Gym','Grocery Store','Pharmacy'])|(Ny_venues['Venue Category'].str.contains('Restaurant')))]
#Counting the Number of Venues in each Category
Neigh_venues=Ny_venues.groupby(['Neighborhood','Venue Category'])['Venue Category'].count()
Neigh_venues=Neigh_venues.to_frame()
#Changing index to column Header
Neigh_venues=Neigh_venues.rename(columns={'Venue Category':'Venue Count'})
Neigh_venues=Neigh_venues.reset_index()
Neigh_venues.head()
Neigh_venues.columns

Index(['Neighborhood', 'Venue Category', 'Venue Count'], dtype='object')

Neigh_venues=Neigh_venues.pivot(index='Neighborhood',columns='Venue Category',values='Venue Count').reset_index().fillna(0)

Neigh_venues.columns.name=None

Neigh_venues.head()

	Neighborhood	Grocery Store	Gym	Pharmacy	Restaurant
0	Allerton	0.0	0.0	0.0	1.0
1	Annadale	0.0	0.0	0.0	2.0
2	Arden Heights	0.0	0.0	1.0	0.0
3	Arlington	0.0	0.0	0.0	1.0
4	Arrochar	0.0	0.0	0.0	2.0

#Creating an empty Data Frame
col_name=['Neighborhood','Gym','Grocery','Pharmacy','Restaurant','Score']
nw_venue_df=pd.DataFrame(columns=col_name)
nw_venue_df

	Neighborhood	Gym	Grocery	Pharmacy	Restaurant	Score

#Calculating the score for each neighboor based on the weigheted average
for i,r in Neigh_venues.iterrows():
    s=r['Gym']*2+r['Grocery Store']*3+r['Pharmacy']*2+r['Restaurant']*3
    sco=s/10
    nw_venue_df=nw_venue_df.append({'Neighborhood':r['Neighborhood'],'Gym':r['Gym'],'Grocery':r['Grocery Store'],'Pharmacy':r['Pharmacy'],'Restaurant':r['Restaurant'],'Score':sco},ignore_index=True)

nw_venue_df.head()

	Neighborhood	Gym	Grocery	Pharmacy	Restaurant	Score
0	Allerton	0.0	0.0	0.0	1.0	0.3
1	Annadale	0.0	0.0	0.0	2.0	0.6
2	Arden Heights	0.0	0.0	1.0	0.0	0.2
3	Arlington	0.0	0.0	0.0	1.0	0.3
4	Arrochar	0.0	0.0	0.0	2.0	0.6

neighcrime_data.rename(columns={'Neighbourhood':'Neighborhood'},inplace=True)
neighcrime_data.head()

	Neighborhood	OFFENSE
0	Laurelton	1
1	Rosedale	1
2	Rosebank	1
3	Roosevelt Island	1
4	Richmond Hill	1

#Combining the both crime dataset along with the venues nearby
NY_DF=pd.merge(neighcrime_data,nw_venue_df,on='Neighborhood',how='left')
NY_DF.head()

	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
0	Laurelton	1	0.0	0.0	0.0	2.0	0.6
1	Rosedale	1	0.0	0.0	1.0	1.0	0.5
2	Rosebank	1	0.0	0.0	0.0	3.0	0.9
3	Roosevelt Island	1	0.0	0.0	0.0	1.0	0.3
4	Richmond Hill	1	0.0	0.0	0.0	2.0	0.6

Now we use the KMeans cluster Algorithm to find the best Neighborhood to live in. For this First we have to determine the value of 'K'. I have used the WSS/Elbow Method and Silhouette Method to determine the best value of K.

#Finding Best Value of K using Elbow Method.
Cluster=NY_DF.drop('Neighborhood',1)
Cluster.dropna(inplace=True)
C=[]
for i in range(1,10):
    KM=KMeans(n_clusters=i,max_iter=500)
    KM.fit(Cluster)
    C.append(KM.inertia_)

plt.plot(range(1,10),C,color='g')
plt.xlabel('Different Values of K')
plt.ylabel('WSS')
plt.show()

At k=3 the above plot has an elbow

#Finding the Best Value of K using the silhouette method
sil=[]
kmax=10
for k in range(2,kmax):
    kmeans=KMeans(n_clusters=k).fit(Cluster)
    labels=kmeans.labels_
    sil.append(silhouette_score(Cluster,labels,metric='euclidean'))

plt.plot(range(1,9),sil,color='g')
plt.xlabel('Different Values of K')
plt.ylabel('Silhouette Score')
plt.show()

At k=3 there is peak in silhouette score

Building the cluster with K=3

#Building cluster with k=3k
clusters=3
cluster_km=KMeans(n_clusters=kclusters,random_state=0).fit(Cluster)
lbl=cluster_km.labels_

#Merging the Cluster Labels with the dataframe
NY_DF.dropna(inplace=True)
NY_DF.insert(0,'Cluster Labels',lbl)
NY_DF.head()

	Cluster Labels	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
0	1	Laurelton	1	0.0	0.0	0.0	2.0	0.6
1	1	Rosedale	1	0.0	0.0	1.0	1.0	0.5
2	1	Rosebank	1	0.0	0.0	0.0	3.0	0.9
3	1	Roosevelt Island	1	0.0	0.0	0.0	1.0	0.3
4	1	Richmond Hill	1	0.0	0.0	0.0	2.0	0.6

nycrime_data.head()
nycrime_data.rename(columns={'Neighbourhood':'Neighborhood'},inplace=True)

NY_merged=pd.merge(nycrime_data,NY_DF,on='Neighborhood')
NY_merged.head()

	DATE	OFFENSE_x	BOROUGH	Crime_Lat	Crime_Lon	Neighborhood	Neigh_Lat	Neigh_Lon	Cluster Labels	OFFENSE_y	Gym	Grocery	Pharmacy	Restaurant	Score
0	2019-04-12	CRIMINAL MISCHIEF & RELATED OF	MANHATTAN	40.764007	-73.996005	Clinton	40.759101	-73.996119	0	12	0.0	0.0	0.0	1.0	0.3
1	2019-04-12	ASSAULT 3 & RELATED OFFENSES	MANHATTAN	40.755959	-73.990729	Clinton	40.759101	-73.996119	0	12	0.0	0.0	0.0	1.0	0.3
2	2019-04-12	PETIT LARCENY	MANHATTAN	40.759794	-73.995854	Clinton	40.759101	-73.996119	0	12	0.0	0.0	0.0	1.0	0.3
3	2019-04-12	CRIMINAL MISCHIEF & RELATED OF	MANHATTAN	40.762299	-73.986107	Clinton	40.759101	-73.996119	0	12	0.0	0.0	0.0	1.0	0.3
4	2019-04-12	MISCELLANEOUS PENAL LAW	MANHATTAN	40.756266	-73.990501	Clinton	40.759101	-73.996119	0	12	0.0	0.0	0.0	1.0	0.3

#Creating Map
#create a map of Toronoto
cluster_map=folium.Map(location=[40.71, -74.0],zoom_start=11)
#Set color scheme for the clusters
x=np.arange(kclusters)
ys=[i+x+(i*x)**2 for i in range(kclusters)]
colors_array=cm.rainbow(np.linspace(0,1,len(ys)))
rainbow=[colors.rgb2hex(i) for i in colors_array]

#add markers on top
for lat,lng,poi,cluster in zip(NY_merged['Neigh_Lat'],NY_merged['Neigh_Lon'],NY_merged['Neighborhood'],NY_merged['Cluster Labels']):
   # label='{},{}'.format(neighbourhood,borough)
    label=folium.Popup(str(poi)+'Cluster'+str(cluster),parse_html=True)
    folium.CircleMarker(
        [lat,lng],
        radius=5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7,
        parse_html=False
    ).add_to(cluster_map)
cluster_map

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NY_DF.loc[NY_DF['Cluster Labels']==0]

	Cluster Labels	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
195	0	Far Rockaway	11	0.0	0.0	0.0	2.0	0.6
196	0	Midtown	11	0.0	0.0	0.0	2.0	0.6
197	0	Concourse	11	0.0	0.0	0.0	2.0	0.6
198	0	Manhattanville	11	0.0	0.0	0.0	1.0	0.3
199	0	Bedford Stuyvesant	11	0.0	0.0	0.0	1.0	0.3
200	0	High Bridge	11	1.0	0.0	2.0	0.0	0.6
201	0	Clinton	12	0.0	0.0	0.0	1.0	0.3
202	0	Hamilton Heights	12	0.0	0.0	0.0	1.0	0.3
203	0	East Williamsburg	13	0.0	0.0	0.0	1.0	0.3
204	0	Brownsville	13	0.0	0.0	0.0	1.0	0.3
205	0	Bushwick	14	0.0	0.0	0.0	3.0	0.9
206	0	Sunset Park	14	0.0	0.0	0.0	4.0	1.2
207	0	Mott Haven	14	1.0	0.0	0.0	1.0	0.5
208	0	Weeksville	14	0.0	0.0	0.0	1.0	0.3
209	0	East Tremont	15	0.0	0.0	0.0	1.0	0.3
210	0	Midtown South	15	0.0	0.0	0.0	1.0	0.3
211	0	Concourse Village	15	0.0	0.0	0.0	2.0	0.6
212	0	Central Harlem	16	0.0	0.0	0.0	2.0	0.6
213	0	Washington Heights	20	0.0	0.0	0.0	2.0	0.6
214	0	Melrose	21	1.0	0.0	0.0	0.0	0.2
215	0	East Harlem	23	0.0	0.0	0.0	3.0	0.9

NY_DF.loc[NY_DF['Cluster Labels']==1]

	Cluster Labels	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
0	1	Laurelton	1	0.0	0.0	0.0	2.0	0.6
1	1	Rosedale	1	0.0	0.0	1.0	1.0	0.5
2	1	Rosebank	1	0.0	0.0	0.0	3.0	0.9
3	1	Roosevelt Island	1	0.0	0.0	0.0	1.0	0.3
4	1	Richmond Hill	1	0.0	0.0	0.0	2.0	0.6
...	...	...	...	...	...	...	...	...
124	1	Ocean Hill	4	0.0	0.0	0.0	2.0	0.6
126	1	Woodlawn	4	0.0	0.0	0.0	2.0	0.6
127	1	West Village	4	0.0	0.0	0.0	3.0	0.9
128	1	Bronxdale	4	0.0	0.0	0.0	3.0	0.9
129	1	Bedford Park	4	0.0	0.0	0.0	1.0	0.3

103 rows × 8 columns

NY_DF.loc[NY_DF['Cluster Labels']==2]    

	Cluster Labels	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
131	2	Williamsburg	5	1.0	0.0	0.0	0.0	0.2
132	2	Baychester	5	0.0	0.0	0.0	1.0	0.3
133	2	Westchester Square	5	0.0	0.0	0.0	3.0	0.9
134	2	Downtown	5	0.0	1.0	0.0	1.0	0.6
135	2	Schuylerville	5	0.0	0.0	0.0	2.0	0.6
136	2	Canarsie	5	1.0	0.0	0.0	2.0	0.8
137	2	Carnegie Hill	5	1.0	0.0	0.0	0.0	0.2
138	2	South Jamaica	5	0.0	1.0	0.0	2.0	0.9
139	2	Soundview	5	0.0	1.0	1.0	1.0	0.8
141	2	Chinatown	5	0.0	0.0	0.0	1.0	0.3
144	2	Allerton	5	0.0	0.0	0.0	1.0	0.3
145	2	North Side	5	0.0	0.0	0.0	1.0	0.3
147	2	Woodside	5	0.0	0.0	0.0	3.0	0.9
149	2	Little Italy	5	0.0	0.0	0.0	2.0	0.6
150	2	Longwood	5	0.0	1.0	0.0	1.0	0.6
151	2	Erasmus	5	0.0	0.0	0.0	2.0	0.6
152	2	Morris Heights	5	0.0	1.0	1.0	1.0	0.8
153	2	Mount Eden	5	1.0	0.0	0.0	1.0	0.5
154	2	Murray Hill	5	0.0	0.0	0.0	6.0	1.8
155	2	Fort Greene	5	0.0	0.0	0.0	3.0	0.9
156	2	Financial District	6	0.0	0.0	0.0	1.0	0.3
157	2	Springfield Gardens	6	1.0	0.0	0.0	0.0	0.2
158	2	Cambria Heights	6	0.0	0.0	1.0	2.0	0.8
159	2	Boerum Hill	6	0.0	0.0	0.0	3.0	0.9
160	2	Upper West Side	6	0.0	0.0	0.0	4.0	1.2
161	2	Williamsbridge	6	0.0	0.0	0.0	1.0	0.3
163	2	Auburndale	6	0.0	0.0	1.0	1.0	0.5
164	2	Broadway Junction	6	0.0	0.0	0.0	1.0	0.3
165	2	Rochdale	6	0.0	0.0	0.0	1.0	0.3
166	2	Flushing	6	1.0	0.0	0.0	2.0	0.8
167	2	New Lots	6	0.0	1.0	0.0	0.0	0.3
168	2	East Village	6	0.0	0.0	0.0	3.0	0.9
169	2	Lower East Side	7	0.0	0.0	0.0	2.0	0.6
170	2	Borough Park	7	0.0	0.0	0.0	2.0	0.6
171	2	University Heights	7	0.0	1.0	0.0	1.0	0.6
172	2	Prospect Lefferts Gardens	7	0.0	0.0	0.0	1.0	0.3
173	2	Rugby	7	0.0	0.0	1.0	1.0	0.5
174	2	Elmhurst	7	0.0	0.0	0.0	4.0	1.2
175	2	North Corona	7	0.0	0.0	0.0	1.0	0.3
177	2	Brighton Beach	8	0.0	0.0	0.0	1.0	0.3
178	2	Belmont	8	0.0	0.0	0.0	1.0	0.3
179	2	Bensonhurst	8	0.0	0.0	0.0	2.0	0.6
180	2	Jackson Heights	8	0.0	1.0	0.0	4.0	1.5
181	2	Astoria	8	1.0	0.0	0.0	2.0	0.8
184	2	Jamaica Center	8	1.0	0.0	0.0	2.0	0.8
185	2	Flatlands	8	0.0	0.0	0.0	1.0	0.3
186	2	Claremont Village	8	1.0	1.0	0.0	1.0	0.8
188	2	Sutton Place	9	1.0	1.0	0.0	0.0	0.5
189	2	Flatiron	9	1.0	0.0	0.0	0.0	0.2
191	2	Chelsea	9	0.0	0.0	0.0	3.0	0.9
192	2	Fordham	10	1.0	0.0	0.0	1.0	0.5
193	2	Prospect Heights	10	0.0	0.0	0.0	2.0	0.6
194	2	East New York	10	1.0	0.0	0.0	0.0	0.2

The careful examination of all the 3 clusters indicates that the Cluster 1 has neighborhoods with best amenities score and moderate amount of crime rate.

Best_Cluster=NY_DF.loc[NY_DF['Cluster Labels']==1].sort_values(by='Score',ascending=False)

Best_Cluster

	Cluster Labels	Neighborhood	OFFENSE	Gym	Grocery	Pharmacy	Restaurant	Score
66	1	Sunnyside	2	1.0	1.0	0.0	4.0	1.7
118	1	Ridgewood	4	0.0	0.0	0.0	4.0	1.2
102	1	Jamaica Hills	3	0.0	0.0	0.0	4.0	1.2
70	1	Kensington	2	0.0	1.0	0.0	3.0	1.2
80	1	Rego Park	2	0.0	0.0	0.0	4.0	1.2
...	...	...	...	...	...	...	...	...
88	1	Wingate	3	0.0	0.0	1.0	0.0	0.2
101	1	Yorkville	3	1.0	0.0	0.0	0.0	0.2
121	1	Midwood	4	0.0	0.0	1.0	0.0	0.2
43	1	Georgetown	2	0.0	0.0	1.0	0.0	0.2
60	1	Arden Heights	2	0.0	0.0	1.0	0.0	0.2

103 rows × 8 columns

From the above cluster it is clear that Sunnyside is the best neighborhood to live in with having only 2 offences reported in past one year and having a amenities score of 1.7

# Getting sunnyside Locations.
SunnySide=NY_merged.loc[NY_merged['Neighborhood']=='Sunnyside']
SunnySide
Sunnyside_venues=Ny_venues.loc[Ny_venues['Neighborhood']=='Sunnyside']
Sunnyside_venues

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
696	Sunnyside	40.740176	-73.926916	Fish House	40.740322	-73.923142	Restaurant
698	Sunnyside	40.740176	-73.926916	Don Pollo II	40.740049	-73.923763	Restaurant
699	Sunnyside	40.740176	-73.926916	I Love Paraguay	40.741087	-73.921490	Restaurant
1076	Sunnyside	40.612760	-74.097126	Mignosi's Supermarket	40.612132	-74.099716	Grocery Store
1077	Sunnyside	40.612760	-74.097126	The RoadHouse	40.613532	-74.100814	Restaurant
1079	Sunnyside	40.612760	-74.097126	Spiro Sports Center Gym	40.615470	-74.095453	Gym

Sunnyside_map=folium.Map(location=[40.740176, -74.097126],zoom_start=8,width=800,height=500)
incidents=folium.plugins.MarkerCluster().add_to(Sunnyside_map)
for lat,lng,label in zip(SunnySide.Crime_Lat,SunnySide.Crime_Lon,SunnySide.OFFENSE_x):
    folium.features.CircleMarker(
    [lat,lng],radius=5,color='red',fill=True,fill_color='blue',fill_opacity=0.6,popup=label).add_to(incidents)
for lat,lng,label in zip(Sunnyside_venues['Venue Latitude'],Sunnyside_venues['Venue Longitude'],Sunnyside_venues['Venue']):
    print(label)
    folium.Marker(
    [lat,lng],popup=label).add_to(Sunnyside_map)

Fish House
Don Pollo II
I Love Paraguay
Mignosi's Supermarket
The RoadHouse
Spiro Sports Center Gym

Sunnyside_map

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